Fatigue detecting method based on deep space-time network

A space-time network and fatigue detection technology, applied in the field of machine learning, can solve the problems of inaccurate classification of fatigue degree and inconvenient operation, and achieve the effect of improving eye representation ability, accurate feature representation, and fast and convenient detection process

Active Publication Date: 2019-05-21
MINJIANG UNIV
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

[0005] To this end, the embodiment of the present invention provides a fatigue detection method based on a deep spatio-temporal network to solve the problems that the fatigue detection method in the prior art cannot accurately classify the degree of fatigue and is inconvenient to operate.

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  • Fatigue detecting method based on deep space-time network
  • Fatigue detecting method based on deep space-time network
  • Fatigue detecting method based on deep space-time network

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Embodiment Construction

[0023] The implementation mode of the present invention is illustrated by specific specific examples below, and those who are familiar with this technology can easily understand other advantages and effects of the present invention from the contents disclosed in this description. Obviously, the described embodiments are a part of the present invention. , but not all examples. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0024] refer to figure 1 , the present embodiment provides a fatigue detection method based on a deep spatio-temporal network, the method comprising:

[0025] S1, simulated driving environment, including driving environment under different weather and road conditions;

[0026] Specifically, the experimenter wears a VR virtual device, presents a virtual driving environment through the VR virtual...

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Abstract

The embodiment of the invention discloses a fatigue detecting method based on a deep space-time network, and relates to the technical field of machine learning. The method comprises the steps that driving environments are simulated, wherein the driving environments comprise driving environments under different weathers and road conditions; sample data is collected and classified under the drivingenvironments, wherein the sample data comprises a first data set and a second data set; a deep space-time network model is constructed, and a feature extractor is generated to extract eye features inthe sample data; the eye features of the first data set are subjected to feature compression by using an automatic codec and input into a recurrent neural network RNN; the second data set is used fortraining the recurrent neural network RNN to obtain a trained fatigue detecting model; the trained fatigue detecting model is used for real-time detection, a detection result is output, and feedback is performed through a warning module. The fatigue detecting method based on the deep space-time network can solve the problems that the fatigue detecting method in the prior art cannot accurately classify the fatigue degrees and is inconvenient to operate.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of machine learning, and in particular to a fatigue detection method based on a deep spatio-temporal network. Background technique [0002] For driving positions, operators need to stay awake. One of the most important factors causing frequent traffic accidents is fatigue driving. After driving continuously for a long time, the driver will experience imbalances in physiological and psychological functions, leading to a decline in driving skills. Fatigue driving can affect various aspects such as driver's attention, sensation, perception, thinking, judgment, will, decision and movement. According to relevant statistics, if the fatigue state of the driver can be detected and the driver can be reminded in time when fatigue driving is found, so that the reaction time can be increased by 0.5 seconds, the possibility of traffic accidents will be reduced by 60%. Therefore, real-time monitori...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): A61B5/18A61B5/11A61B5/0476
Inventor 刘天键
Owner MINJIANG UNIV
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